It is often desirable to restrict access to property or resources to particular individuals. Biometric systems can be used to authenticate the identity of an individual to either grant or deny access to a resource. For example, iris scanners can be used by a biometric security system to identify an individual based on unique structures in the individual's iris. Such a system can erroneously authorize an impostor, however, if the impostor presents for scanning a pre-recorded image or video of the face of an authorized person. Such a fake image or video can be displayed on a monitor such as a cathode ray tube (CRT) or liquid crystal display (LCD) screen, in glossy photographs, etc., held in front of a camera used for scanning. Other spoofing techniques include the use of a photographically accurate three-dimensional mask of a legitimate user's face.
One category of existing anti-spoofing measures focuses primarily on static imagery (e.g., photograph based) attacks. These measures assume that static spoof attacks fail to reproduce naturally occurring and disparate movement of different parts of the image, mostly within the face. They also assume that each of the aforesaid motions in live scans occurs at a different scale in terms of the natural agility and frequency of the associated muscle groups. However, these measures can only detect static (e.g., picture-based) spoof attacks, and need a certain time window of observation at a high enough frame rate to be able to resolve the aforesaid motion vectors to their required velocity and frequency profiles, if any. They can also falsely reject live subjects that are holding very still during the scan, or falsely accept static reproductions with added motion, e.g., by bending and shaking the spoofing photos in certain ways.
A second category of existing anti-spoofing measures assumes that the photographic or video reproduction of the biometric sample is not of sufficient quality and thus image texture analysis methods can give identify the spoof. However, the assumption of discernibly low quality spoof reproduction is not a reliable one, especially with the advent of advanced high quality and exceedingly commonplace high definition recording and display technologies that can even be found in modern smartphones and tablets. Not surprisingly, by relying on specific and technology-dependent spoof reproduction artifacts, such techniques have been shown to be dataset dependent and have demonstrated subpar generalization capabilities. Another category of anti-spoofing measures, related to the second, which is based on reference or no-reference image quality metrics, suffers from the same shortcomings.